PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices

no code implementations • 26 Jan 2023 • Yuji Chai, Devashree Tripathy, Chuteng Zhou, Dibakar Gope, Igor Fedorov, Ramon Matas, David Brooks, Gu-Yeon Wei, Paul Whatmough

The ability to accurately predict deep neural network (DNN) inference performance metrics, such as latency, power, and memory footprint, for an arbitrary DNN on a target hardware platform is essential to the design of DNN based models.

UDC: Unified DNAS for Compressible TinyML Models

no code implementations • 15 Jan 2022 • Igor Fedorov, Ramon Matas, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul Whatmough

Deploying TinyML models on low-cost IoT hardware is very challenging, due to limited device memory capacity.

Model Compression Neural Architecture Search +2

Collapsible Linear Blocks for Super-Efficient Super Resolution

3 code implementations • 17 Mar 2021 • Kartikeya Bhardwaj, Milos Milosavljevic, Liam O'Neil, Dibakar Gope, Ramon Matas, Alex Chalfin, Naveen Suda, Lingchuan Meng, Danny Loh

Our results highlight the challenges faced by super resolution on AI accelerators and demonstrate that SESR is significantly faster (e. g., 6x-8x higher FPS) than existing models on mobile-NPU.

4k 8k +1